takehome03

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

packages = c('ggiraph', 'plotly', 
             'DT', 'patchwork',
             'gganimate', 'tidyverse',
             'readxl', 'gifski', 'gapminder')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

## 载入需要的程辑包：ggiraph

## Warning: 程辑包'ggiraph'是用R版本4.1.2 来建造的

## 载入需要的程辑包：plotly

## 载入需要的程辑包：ggplot2

## 
## 载入程辑包：'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

## 载入需要的程辑包：DT

## 载入需要的程辑包：patchwork

## Warning: 程辑包'patchwork'是用R版本4.1.2 来建造的

## 载入需要的程辑包：gganimate

## 载入需要的程辑包：tidyverse

## Warning: 程辑包'tidyverse'是用R版本4.1.3 来建造的

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v tibble  3.1.4     v dplyr   1.0.8
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1
## v purrr   0.3.4

## Warning: 程辑包'dplyr'是用R版本4.1.3 来建造的

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks plotly::filter(), stats::filter()
## x dplyr::lag()    masks stats::lag()

## 载入需要的程辑包：readxl

## Warning: 程辑包'readxl'是用R版本4.1.2 来建造的

## 载入需要的程辑包：gifski

## 载入需要的程辑包：gapminder

## Warning: 程辑包'gapminder'是用R版本4.1.2 来建造的

Including Plots

You can also embed plots, for example:

## Rows: 322 Columns: 7-- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (4): ID, CLASS, GENDER, RACE
## dbl (3): ENGLISH, MATHS, SCIENCE
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot") +
  scale_y_continuous(NULL, 
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS, tooltip= ID), ## data_id = class means you will select all students from the same class
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(
    opts_hover(css = "fill: #202020;"),
    opts_hover_inv(css = "opacity:0.2;")
  )
)

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school", as.character(exam_data$ID) )
p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)

p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) +
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(code = print(p1/p2),
       width_svg = 6,
       height_svg = 6,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       )

plot_ly(data= exam_data,
        x = ~ MATHS,
        y = ~ ENGLISH,
        color = ~RACE,
        colors = "Set1") #COLOR GIVING THE ATTRIBUTE AND COLORS GIVES COLOR IN REAL MEANING 

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

globalPop <- read_xls("data/GlobalPopulation.xls")

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +
  ease_aes('linear')